Getting Actionable Insights from Power BI

You'll take the role of a business analyst and create factful, truthful, engaging, and visually appealing reports with meaningful and actionable insights, and analyze and present data in a way that promotes actionable insights, strong decisions, and clear data-driven recommendations.

Most courses focus on software, a programming language, with little attention to how to use visual clues and analyze and present data in a way that provides insights that can be followed up.

This no-nonsense, hands-on course will help you create well-designed, truthful, insightful, and actionable reports using Power BI, an industry-standard data analysis tool. You will process and clean realistic data, extract meaning from it, and communicate it using charts and visualizations. You'll do all this by using Power BI and its built-in DAX language.

By the end of the course, you will be able to use Power BI effectively as a business analytics tool to load, pre-process, and model data and create meaningful and actionable reports and visualizations.

What You Will Learn

• Perform exploratory analysis in the context of creating an actionable report
• Load, process, and clean data to make an engaging data analysis
• Correctly select right chart type for your data
• Extract a core message from data and communicate this message using visualizations
• Use Power BI to create factful and engaging data reports
• Work with Power Query, DAX, and standard Power BI visualizations to create insightful and actionable reports in Power BI

Audience

This course is for data professionals, analysts, and students of business intelligence and data science who already know the fundamentals of working with data. Basic knowledge of Power BI is assumed. Being able to load data from an Excel file on a laptop and add a simple bar chart is sufficient for this course.

About The Author

Nikita Barsukov: Nikita Barsukov is a software developer and data scientist with 10 years' experience in the industry. He is a self-taught data scientist who learned early on that it is often messaging, insight and data storytelling that is important in data science, not technology or software. He went through a traditional data science path at the beginning of his career—for example, learning R and machine learning algorithms using online courses. Then he also quickly grasped the value of visual data storytelling, using charts as a way to communicate and tell something. He lives in Copenhagen, Denmark with his beautiful wife and three awesome sons.